Activity counts measure attention, not model quality. Missing counts are unknown. Publication status follows the linked source; a listed venue alone does not confirm peer review.
- Form factor
- Other
- Signals
- PPG, ECG
- License
- MIT
- Type
- Task model
- Form factor
- Other
- Parameters
- Not reported
- Architecture
- Not reported
- Venue
- Not reported
- Year
- Not reported
- arXiv
- Not reported
- DOI
- Not reported
- Weights
- Not released
- Licence
- MIT (opens in a new tab)
- Caveats
- Not reported
- Datasets
- Not reported
- Added
- Oct 19, 2026
- Updated
- Oct 19, 2026
Original description
CardioFusion-AI is an open-source research framework for multimodal ECG–PPG physiological signal processing, sensor fusion, and cardiovascular monitoring research.
The project combines Electrocardiogram (ECG) and Photoplethysmography (PPG) signals through adaptive multimodal sensor fusion, enabling robust cardiovascular monitoring suitable for next-generation Wearable Body Area Networks (WBANs).
The framework focuses on building clinically interpretable, computationally efficient AI models that can eventually be deployed on wearable and edge devices.
Verbatim fromGitHub· Description from the repository README; licence: NOASSERTION
How it works
- Inputs
- PPG, ECG
- Approach
- code release · no weights
Datasets
Cite
APA
CardioFusion-AI. (n.d.).
BibTeX
@misc{cardiofusionaicardiofusionai,
title = {{CardioFusion-AI}}
}Built from the hub record. Check it against the publisher before submitting.
History
Added to the ChatPPG research hub